carpet_concentrations.gridders.latitude_seasonality_gridder

Gridder based on pre-calculated latitudinal gradient and seasonality

LatitudeSeasonalityGridder

class LatitudeSeasonalityGridder(gridding_values, seasonality_name='seasonality', latitudinal_gradient_name='latitudinal_gradient')[source]

Bases: object

Gridder based on specified latitudinal gradient and seasonality

calculate(global_means)[source]

Calculate gridded values

Parameters

global_means (xarray.core.dataset.Dataset) – Global-mean values. These should have already been interpolated such that their dimensions are ("year", "month") (other dimensions e.g. "scenario" are also ok) to obtain sensible results (if not, there may be jumps at the start and end of years). global_means should have also already been turned into a pint-compatiable quantity using pint.quantify or similar.

Returns

xarray.core.dataset.Dataset – Gridded values on a ("latitude", "year", "month") grid (plus any other dimensions present in global_means)

Raises

CoordinateError – global_means does not have at least the dimensions of ("year", "month") (other dimensions e.g. "scenario" are also ok)
NotPintQuantifiedError – global_means has not been quantified using pint.quantify or similar

Notes

Eq. 1 of Meinshausen et al.1. We use slightly different notation here for clarity.

\[C(l, m, y, ...) = \overline{C(m, y, ...)} + S(l, m, y) + L(l, m, y)\]

i.e. the concentration at latitude \(l\) in year \(y\) and month \(m\) is the sum of the global-, annual-mean concentration in that year (already interpolated down to a monthly timestep), the seasonality at that latitude in that year in that month (varies with year and latitude as often scaled with something else) and the latitudinal gradient at that latitude in that year and month (varies with year and month as often scaled with something else, must have monthly information to avoid step changes in output). Note that the global-, annual-mean concentrations \(C(y, m)\) must be pre-interpolated onto monthly steps using a mean-preserving alogrithm [#11 for function which will do this] to avoid spurious steps in the outputs.

The ellipses represent extra dimensions (e.g. scenario, greenhouse gas) that might be in \(C(y, m)\). Any such dimensions are preserved in the output.

gridding_values: xarray.core.dataset.Dataset

Values to use to convert global-mean concentrations to a grid

This should have already been turned into a pint-compatiable quantity using pint.quantify or similar. It should also contain variables that match the value of seasonality_name and latitudinal_gradient_name. Both the variables must have at least the dimensions (“year”, “month”, “lat”). The seasonality variable must have an annual-mean of zero. The latitudinal gradient must have a spatial-mean of zero.

latitudinal_gradient_name: str: Name of the latitudinal gradient variable (in gridding_values but also used elsewhere)

seasonality_name: str: Name of the seasonality variable (in gridding_values but also used elsewhere)

1: M. Meinshausen, E. Vogel, A. Nauels, K. Lorbacher, N. Meinshausen, D. M. Etheridge, P. J. Fraser, S. A. Montzka, P. J. Rayner, C. M. Trudinger, P. B. Krummel, U. Beyerle, J. G. Canadell, J. S. Daniel, I. G. Enting, R. M. Law, C. R. Lunder, S. O’Doherty, R. G. Prinn, S. Reimann, M. Rubino, G. J. M. Velders, M. K. Vollmer, R. H. J. Wang, and R. Weiss. Historical greenhouse gas concentrations for climate modelling (cmip6). Geoscientific Model Development, 10(5):2057–2116, 2017. URL: https://gmd.copernicus.org/articles/10/2057/2017/, doi:10.5194/gmd-10-2057-2017.